The contribution of gross primary production of understory dwarf bamboo, Sasa senanensis, in a cool-temperate deciduous broadleaved forest in central Japan

Temperate deciduous forests often consist of the mixed species and multi-layered canopies that include taller vegetation layers (overstory trees) and subordinate layers (understory vegetations). We attempted to describe the seasonal changes in gross primary production (GPP) in a cool-temperate deciduous broadleaf forest in central Japan using a process-based model, and to determine the contribution of the understory vegetation to whole ecosystem carbon balance. Leaf phenology and seasonal trends in photosynthetic characteristics differed among species. GPP of overstory trees was reflected by an increase in the higher maximum photosynthetic rate and the amount of foliage. On the other hand, understory Sasa, which grew under light-limited conditions, was associated with high photosynthetic capacity even at a low air temperature, allowing it to take full advantage of leafless periods of overstory canopies. Additionally, understory Sasa maintained healthy growth by physiological acclimation, having a higher apparent quantum yield, which allowed maximized utilization of the available sunlight. Such growth patterns were well suited to their strategy of niche occupancy in the forest ecosystem. Consequently, the annual GPP of the overstory trees and understory Sasa were estimated as 78.1 ± 34.2 and 26.1 ± 6.8 mol m−2 year−1, respectively. The contribution of GPP of understory Sasa to the entire forest was approximately 25%. This result indicates that understory Sasa is as important as overstory trees in determining the sink/source function of forest ecosystems. In order to validate the modeling approach, we compared the modeled GPP values with independent measurements obtained using the eddy covariance technique at the same site. Despite differences in the methods and analytical techniques, GPP values at the stand level were in good agreement (r2 = 0.83, P < 0.0001, n = 195). This demonstrates the potential of the process-based model for scaling-up GPP measurements obtained at the CO2 flux tower.